support wan full training

support wan full train

examples/wanvideo/README.md

@@ -42,7 +42,7 @@ https://github.com/user-attachments/assets/c0bdd5ca-292f-45ed-b9bc-afe193156e75
 
 ## Train
 
-We support Wan-Video LoRA training. Here is a tutorial.
+We support Wan-Video LoRA training and full training. Here is a tutorial.
 
 Step 1: Install additional packages
 
@@ -99,9 +99,12 @@ data/example_dataset/
 
 Step 4: Train
 
+LoRA training:
+
 ```shell
 CUDA_VISIBLE_DEVICES="0" python examples/wanvideo/train_wan_t2v.py \
   --task train \
+  --train_architecture lora \
   --dataset_path data/example_dataset \
   --output_path ./models \
   --dit_path "models/Wan-AI/Wan2.1-T2V-1.3B/diffusion_pytorch_model.safetensors" \
@@ -115,8 +118,26 @@ CUDA_VISIBLE_DEVICES="0" python examples/wanvideo/train_wan_t2v.py \
   --use_gradient_checkpointing
 ```
 
+Full training:
+
+```shell
+CUDA_VISIBLE_DEVICES="0" python examples/wanvideo/train_wan_t2v.py \
+  --task train \
+  --train_architecture full \
+  --dataset_path data/example_dataset \
+  --output_path ./models \
+  --dit_path "models/Wan-AI/Wan2.1-T2V-1.3B/diffusion_pytorch_model.safetensors" \
+  --steps_per_epoch 500 \
+  --max_epochs 10 \
+  --learning_rate 1e-4 \
+  --accumulate_grad_batches 1 \
+  --use_gradient_checkpointing
+```
+
 Step 5: Test
 
+Test LoRA:
+
 ```python
 import torch
 from diffsynth import ModelManager, WanVideoPipeline, save_video, VideoData
@@ -129,16 +150,39 @@ model_manager.load_models([
     "models/Wan-AI/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth",
 ])
 model_manager.load_lora("models/lightning_logs/version_1/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0)
-
 pipe = WanVideoPipeline.from_model_manager(model_manager, device="cuda")
 pipe.enable_vram_management(num_persistent_param_in_dit=None)
 
-# Text-to-video
 video = pipe(
     prompt="...",
     negative_prompt="...",
     num_inference_steps=50,
     seed=0, tiled=True
 )
-save_video(video, "video_with_lora.mp4", fps=30, quality=5)
+save_video(video, "video.mp4", fps=30, quality=5)
+```
+
+Test fine-tuned base model:
+
+```python
+import torch
+from diffsynth import ModelManager, WanVideoPipeline, save_video, VideoData
+
+
+model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu")
+model_manager.load_models([
+    "models/lightning_logs/version_1/checkpoints/epoch=0-step=500.ckpt",
+    "models/Wan-AI/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth",
+    "models/Wan-AI/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth",
+])
+pipe = WanVideoPipeline.from_model_manager(model_manager, device="cuda")
+pipe.enable_vram_management(num_persistent_param_in_dit=None)
+
+video = pipe(
+    prompt="...",
+    negative_prompt="...",
+    num_inference_steps=50,
+    seed=0, tiled=True
+)
+save_video(video, "video.mp4", fps=30, quality=5)
 ```

examples/wanvideo/train_wan_t2v.py

@@ -134,7 +134,7 @@ class TensorDataset(torch.utils.data.Dataset):
 
 
 class LightningModelForTrain(pl.LightningModule):
-    def __init__(self, dit_path, learning_rate=1e-5, lora_rank=4, lora_alpha=4, lora_target_modules="q,k,v,o,ffn.0,ffn.2", init_lora_weights="kaiming", use_gradient_checkpointing=True):
+    def __init__(self, dit_path, learning_rate=1e-5, lora_rank=4, lora_alpha=4, train_architecture="lora", lora_target_modules="q,k,v,o,ffn.0,ffn.2", init_lora_weights="kaiming", use_gradient_checkpointing=True):
         super().__init__()
         model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu")
         model_manager.load_models([dit_path])
@@ -142,13 +142,16 @@ class LightningModelForTrain(pl.LightningModule):
         self.pipe = WanVideoPipeline.from_model_manager(model_manager)
         self.pipe.scheduler.set_timesteps(1000, training=True)
         self.freeze_parameters()
-        self.add_lora_to_model(
+        if train_architecture == "lora":
-            self.pipe.denoising_model(),
+            self.add_lora_to_model(
-            lora_rank=lora_rank,
+                self.pipe.denoising_model(),
-            lora_alpha=lora_alpha,
+                lora_rank=lora_rank,
-            lora_target_modules=lora_target_modules,
+                lora_alpha=lora_alpha,
-            init_lora_weights=init_lora_weights,
+                lora_target_modules=lora_target_modules,
-        )
+                init_lora_weights=init_lora_weights,
+            )
+        else:
+            self.pipe.denoising_model().requires_grad_(True)
         
         self.learning_rate = learning_rate
         self.use_gradient_checkpointing = use_gradient_checkpointing
@@ -384,6 +387,13 @@ def parse_args():
         action="store_true",
         help="Whether to use gradient checkpointing.",
     )
+    parser.add_argument(
+        "--train_architecture",
+        type=str,
+        default="lora",
+        choices=["lora", "full"],
+        help="Model structure to train. LoRA training or full training.",
+    )
     args = parser.parse_args()
     return args
 
@@ -434,6 +444,7 @@ def train(args):
     model = LightningModelForTrain(
         dit_path=args.dit_path,
         learning_rate=args.learning_rate,
+        train_architecture=args.train_architecture,
         lora_rank=args.lora_rank,
         lora_alpha=args.lora_alpha,
         lora_target_modules=args.lora_target_modules,